Segmenting image data such as, for example, image data from a computer tomography scanner in order to detect a liver in a subject under examination such as, for example, a person, represents a fundamental preprocessing step for a number of applications. Thus, segmenting image data for detecting the liver is helpful, for example, in planning a surgical intervention or in an image-directed liver treatment.
However, automatic and accurate segmentation of image data for liver detection provides a number of problems. The size and shape of the liver can be very different in dependence on the patient and the type of illness. In particular, a cirrhosis of the liver or a tumor inside the liver influence the size and shape of the liver in a scarcely predictable manner. In addition, the signal values which are detected, for example, by a computer tomography scanner can vary over a wide range inside the liver and the surrounding area in dependence on basic scanner parameters and a quantity of a contrast agent administered. Segmentation of the image data is very difficult particularly at the transition between liver tissue and muscle tissue between the ribs because of the similar X-ray absorption of the liver tissue and the musculature. In addition, tumors or a fatty liver illness lead to greatly different signal values of computer tomography image data inside the liver.
A general method for segmenting image data is disclosed in US 2006/0147126 A1 which carries out image data segmenting with the aid of the so-called Random Walker method on the basis of a theory of graphs. The Random Walker method operates in such a manner that a user initially provides some pixels with markings. At least one pixel which is located inside the liver is marked as a liver pixel (liver seed point) and at least one further pixel which does not belong to the liver is marked as background seed point. Segmenting of the image data is then determined by the Random Walker method as follows: for each pixel, the probability is calculated that a Random Walker which starts at this pixel reaches a pixel with a marking.
The direction in which the Random Walker moves is random but the probability for a direction of movement can be influenced by weights between two adjacent pixels. The more similar two pixels are (for example the more similar the signal values of two adjacent pixels are) the greater is the probability that the Random Walker selects this transition. The marking having the greatest probability is then allocated to the pixel. Instead of a real Random Walker simulation, the probabilities can be calculated analytically as indicated in US Patent No. 2006/0147126 A1.
Problems of the aforementioned Random Walker method are, on the one hand, to automatically determine a suitable selection of pixels which are allocated with the greatest probability, for example to the liver or to the background, respectively, and to provide a suitable weight function. In addition, an analytic calculation of the Random Walker method requires considerable computing expenditure in the case of large image data volumes which can lead to undesirably long waiting times during the segmenting.